Ensembled Semi Supervised Clustering Approach for High Dimensional Data
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
we first propose an incremental semi-supervised clustering ensemble framework (ISSCE) that makes use of the advantage of the random subspace technique, the constraint propagation approach, the incremental ensemble member selection process, and the normalized cut algorithm to perform high dimensional data clustering. The random subspace technique is effective for handling high dimensional data, while the constraint propagation approach is useful for incorporating prior knowledge. The incremental ensemble member selection process is newly designed to remove redundant ensemble members based on a newly proposed local cost function and a global cost function, and the normalized cut algorithm is adopted to serve as the consensus function for providing more stable, robust, and accurate results.
Authors and Affiliations
M. LeelaReddy, Naveen Sai, G. Keerthi, M. Druga Kalyani
Keywords
Related Articles
- EP ID EP23916
- DOI http://doi.org/10.22214/ijraset.2017.4210
- Views 279
- Downloads 9
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